Conventional digital camera collection architectures collect images, one at a time, to satisfy a user requirement. As the breadth of user requirements widens, the camera is designed to collect images with various trade-offs. For overhead surveillance systems, the users prefer high-resolution images covering a very large field-of-view (FOV) at video rates. Unfortunately, downlink bandwidth is generally limited, which prohibits the camera from capturing and downlinking very large FOVs at high resolutions and at video rates. The camera collection architecture is designed, therefore, to allow FOV, frame rate, and resolution to be traded. The requests by the different users are prioritized in order of importance and the camera is configured to first capture the image data for the user with the highest priority. To increase flexibility a zoom lens may be added that enables the user to trade between resolution and FOV.
If two users have different data requirements from the same camera at the same time, the camera cannot accommodate both users simultaneously. A decision needs to be made as to which user has priority. For example, if one user requires a large area imaged at the cost of lower resolution, while another user requires a high resolution at the cost of a smaller area, both users cannot be satisfied simultaneously. In addition, each user cannot independently control the image collection process, without impacting the images requested by the other user. Increasing both the FOV and resolution creates bandwidth, storage and processing problems for both users.
With advancement in processing power, digital imaging sensors and storage devices, paying for collecting, storing, and processing images is now significantly cheaper than paying for individual camera collections and dissemination. For example, when televising a sporting event, there are several cameras viewing different locations at different angles. Each camera may have a different task (for example: follow the quarterback, follow the running back). There are also several hundred newspaper photographers collecting still imagery that are viewing different events on the sporting field. Paying for all these different camera collections is expensive.
As the technology of imaging becomes cheaper, a person may be replaced with a set of imaging systems that collects data at the same resolution over a greater area of view. For example, a blimp may collect an entire football field at ¼ inch resolution (approximately 17,280x8,640 pixels). A television controller may select the HDTV region (1,920x1,080 pixels) and time of interest before the signals are broadcast. The imagery may also be stored, so that if the television controller misses a critical part of a football play, he may go back in time and select a new region of interest for a replay broadcast.
It is probably cheaper to place multiple sensors or cameras in one imaging system, collect data over a large FOV and select smaller regions of the FOV for dissemination to an end user, as compared to the cost of having several cameramen pointing individual cameras to capture multiple scenes on the football field. Similarly, it is cheaper to place multiple cameras in one imaging system for collecting data over a large FOV in situations pertaining to security, where a region of interest may be selected based on the occurrence of an event (for example: door opening, cash register opening, loud noise, bright flash, or activation of a panic button). Multiple cameras for viewing a large FOV may also be efficiently used in border security situations, where regions of interest may be identified by motion sensors or IR signatures. Such multiple cameras having a large FOV may also be efficiently used in reconnaissance systems that require persistent surveillance of a scene.
The present invention may advantageously be used in all of the above described situations. As will be explained, present invention uses compression technology and intelligent bandwidth management within the camera architecture. This allows multiple users to simultaneously view different image data, in real time, at an acceptable downlink bandwidth, without impacting image data requested by other users.